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manufacturing practices with real-world industry application. This full PhD scholarship is based at Swinburne’s School of Science, Computing and Engineering Technologies in Melbourne. The successful candidate
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Vision and Edge Computing'. PhD candidates involved in this project will be trained in the emerging field of smart infrastructure, which is critical for Australian society in the coming decade
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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. The underwater acoustic communication technologies will help. The school is focusing on research in AI/machine learning and signal processing which are the research areas in this proposed project. We have
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technologies, social structures, and networks. Effective C2 organisational systems are critical not only to military settings, but also to the operation of many civil domains, including emergency response
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. Understanding of or curiosity about machine learning, AI, or cloud computing tools used in agricultural analytics. Interest or experience in working with industry, government, or multidisciplinary research teams
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Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of Adelaide, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data
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models for structural health monitoring of civil engineering structures. Digital twin models are used to interpret real time information from videos and images aided by computer vision techniques
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reliable. This project will be supported by a robust infrastructure and an intellectually stimulating environment within our machine learning group. The PhD student will be supervised by two highly